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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Melendez, J. Puig, D. Garcia, M.A. |
| Copyright Year | 2010 |
| Abstract | An inherent problem of unsupervised texture segmentation is the absence of previous knowledge regarding the texture patterns present in the images to be segmented. A new efficient methodology for unsupervised image segmentation based on texture is proposed. It takes advantage of a supervised pixel-based texture classifier trained with feature vectors associated with a set of texture patterns initially extracted through a clustering algorithm. Therefore, the final segmentation is achieved by classifying each image pixel into one of the patterns obtained after the previous clustering process. Multi-sized evaluation windows following a top-down approach are applied during pixel classification in order to improve accuracy. The proposed technique has been experimentally validated on MeasTex, VisTex and Brodatz compositions, as well as on complex ground and aerial outdoor images. Comparisons with state-of the-art unsupervised texture segmenters are also provided. |
| Starting Page | 854 |
| Ending Page | 857 |
| File Size | 668451 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781424475421 |
| ISSN | 10514651 |
| e-ISBN | 9781424475414 |
| DOI | 10.1109/ICPR.2010.215 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2010-08-23 |
| Publisher Place | Turkey |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Image segmentation Pixel Clustering algorithms Accuracy Feature extraction Classification algorithms Image edge detection |
| Content Type | Text |
| Resource Type | Article |
| Subject | Computer Vision and Pattern Recognition |
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